Howtosignalprocessing
- What is homography in image processing?
- What does a homography matrix do?
- How do you use homography matrix?
- How many points do you need to estimate a homography between two images?
What is homography in image processing?
In the field of computer vision, any two images of the same planar surface in space are related by a homography (assuming a pinhole camera model). This has many practical applications, such as image rectification, image registration, or camera motion—rotation and translation—between two images.
What does a homography matrix do?
An example of such a transformation matrix is the Homography. It allows us to shift from one view to another view of the same scene by multiplying the Homography matrix with the points in one view to find their corresponding locations in another view (Equation 1).
How do you use homography matrix?
This spatial relationship is represented by a transformation known as a homography, H, where H is a 3 x 3 matrix. To apply homography H to a point p, simply compute p' = Hp, where p and p' are (3-dimensional) homogeneous coordinates. p' is then the transformed point.
How many points do you need to estimate a homography between two images?
To calculate a homography between two images, you need to know at least 4 point correspondences between the two images. If you have more than 4 corresponding points, it is even better. OpenCV will robustly estimate a homography that best fits all corresponding points.
